Overview

Brought to you by YData

Dataset statistics

Number of variables13
Number of observations3232
Missing cells0
Missing cells (%)0.0%
Duplicate rows448
Duplicate rows (%)13.9%
Total size in memory328.4 KiB
Average record size in memory104.0 B

Variable types

Categorical1
Numeric11
Text1

Alerts

Dataset has 448 (13.9%) duplicate rowsDuplicates
chlorides is highly overall correlated with density and 3 other fieldsHigh correlation
density is highly overall correlated with chloridesHigh correlation
free sulfur dioxide is highly overall correlated with total sulfur dioxide and 1 other fieldsHigh correlation
sulphates is highly overall correlated with typeHigh correlation
total sulfur dioxide is highly overall correlated with chlorides and 3 other fieldsHigh correlation
type is highly overall correlated with chlorides and 4 other fieldsHigh correlation
volatile acidity is highly overall correlated with chlorides and 2 other fieldsHigh correlation
citric acid has 140 (4.3%) zerosZeros

Reproduction

Analysis started2024-09-06 18:29:30.891401
Analysis finished2024-09-06 18:29:56.500575
Duration25.61 seconds
Software versionydata-profiling vv4.9.0
Download configurationconfig.json

Variables

type
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size25.4 KiB
Riesling
1633 
Garnacha
1599 

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters25856
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRiesling
2nd rowRiesling
3rd rowRiesling
4th rowRiesling
5th rowRiesling

Common Values

ValueCountFrequency (%)
Riesling 1633
50.5%
Garnacha 1599
49.5%

Length

2024-09-06T15:29:56.699067image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-06T15:29:56.883009image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
riesling 1633
50.5%
garnacha 1599
49.5%

Most occurring characters

ValueCountFrequency (%)
a 4797
18.6%
i 3266
12.6%
n 3232
12.5%
R 1633
 
6.3%
e 1633
 
6.3%
s 1633
 
6.3%
l 1633
 
6.3%
g 1633
 
6.3%
G 1599
 
6.2%
r 1599
 
6.2%
Other values (2) 3198
12.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 25856
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 4797
18.6%
i 3266
12.6%
n 3232
12.5%
R 1633
 
6.3%
e 1633
 
6.3%
s 1633
 
6.3%
l 1633
 
6.3%
g 1633
 
6.3%
G 1599
 
6.2%
r 1599
 
6.2%
Other values (2) 3198
12.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 25856
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 4797
18.6%
i 3266
12.6%
n 3232
12.5%
R 1633
 
6.3%
e 1633
 
6.3%
s 1633
 
6.3%
l 1633
 
6.3%
g 1633
 
6.3%
G 1599
 
6.2%
r 1599
 
6.2%
Other values (2) 3198
12.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 25856
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 4797
18.6%
i 3266
12.6%
n 3232
12.5%
R 1633
 
6.3%
e 1633
 
6.3%
s 1633
 
6.3%
l 1633
 
6.3%
g 1633
 
6.3%
G 1599
 
6.2%
r 1599
 
6.2%
Other values (2) 3198
12.4%

fixed acidity
Real number (ℝ)

Distinct98
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.6648515
Minimum4.6
Maximum15.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size25.4 KiB
2024-09-06T15:29:57.116049image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum4.6
5-th percentile5.9
Q16.7
median7.3
Q38.2
95-th percentile10.7
Maximum15.9
Range11.3
Interquartile range (IQR)1.5

Descriptive statistics

Standard deviation1.5205888
Coefficient of variation (CV)0.19838464
Kurtosis2.9221114
Mean7.6648515
Median Absolute Deviation (MAD)0.7
Skewness1.4356428
Sum24772.8
Variance2.3121904
MonotonicityNot monotonic
2024-09-06T15:29:57.360700image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7.2 153
 
4.7%
7.1 139
 
4.3%
6.8 137
 
4.2%
6.9 132
 
4.1%
7 130
 
4.0%
7.3 128
 
4.0%
7.4 125
 
3.9%
6.4 106
 
3.3%
7.8 101
 
3.1%
6.6 100
 
3.1%
Other values (88) 1981
61.3%
ValueCountFrequency (%)
4.6 1
 
< 0.1%
4.7 1
 
< 0.1%
4.8 2
 
0.1%
4.9 1
 
< 0.1%
5 13
0.4%
5.1 11
0.3%
5.2 14
0.4%
5.3 12
0.4%
5.4 11
0.3%
5.5 7
0.2%
ValueCountFrequency (%)
15.9 1
< 0.1%
15.6 2
0.1%
15.5 2
0.1%
15 2
0.1%
14.3 1
< 0.1%
14.2 1
< 0.1%
14 1
< 0.1%
13.8 1
< 0.1%
13.7 2
0.1%
13.5 1
< 0.1%

volatile acidity
Real number (ℝ)

HIGH CORRELATION 

Distinct171
Distinct (%)5.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.40261448
Minimum0.08
Maximum1.58
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size25.4 KiB
2024-09-06T15:29:57.578746image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.08
5-th percentile0.17
Q10.25
median0.35
Q30.54
95-th percentile0.745
Maximum1.58
Range1.5
Interquartile range (IQR)0.29

Descriptive statistics

Standard deviation0.19118856
Coefficient of variation (CV)0.47486758
Kurtosis0.95180579
Mean0.40261448
Median Absolute Deviation (MAD)0.12
Skewness0.95995496
Sum1301.25
Variance0.036553066
MonotonicityNot monotonic
2024-09-06T15:29:57.819843image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.28 111
 
3.4%
0.26 105
 
3.2%
0.25 101
 
3.1%
0.24 101
 
3.1%
0.27 88
 
2.7%
0.31 80
 
2.5%
0.23 80
 
2.5%
0.32 79
 
2.4%
0.3 78
 
2.4%
0.21 77
 
2.4%
Other values (161) 2332
72.2%
ValueCountFrequency (%)
0.08 4
 
0.1%
0.1 3
 
0.1%
0.105 2
 
0.1%
0.11 4
 
0.1%
0.115 2
 
0.1%
0.12 12
0.4%
0.125 2
 
0.1%
0.13 13
0.4%
0.135 1
 
< 0.1%
0.14 23
0.7%
ValueCountFrequency (%)
1.58 1
 
< 0.1%
1.33 2
0.1%
1.24 1
 
< 0.1%
1.185 1
 
< 0.1%
1.18 1
 
< 0.1%
1.13 1
 
< 0.1%
1.115 1
 
< 0.1%
1.09 1
 
< 0.1%
1.07 1
 
< 0.1%
1.04 3
0.1%

citric acid
Real number (ℝ)

ZEROS 

Distinct84
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.31865408
Minimum0
Maximum1.66
Zeros140
Zeros (%)4.3%
Negative0
Negative (%)0.0%
Memory size25.4 KiB
2024-09-06T15:29:58.088026image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.01
Q10.2175
median0.32
Q30.44
95-th percentile0.62
Maximum1.66
Range1.66
Interquartile range (IQR)0.2225

Descriptive statistics

Standard deviation0.17539225
Coefficient of variation (CV)0.55041582
Kurtosis0.81282036
Mean0.31865408
Median Absolute Deviation (MAD)0.11
Skewness0.21747487
Sum1029.89
Variance0.030762441
MonotonicityNot monotonic
2024-09-06T15:29:58.356383image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.49 234
 
7.2%
0 140
 
4.3%
0.34 113
 
3.5%
0.24 113
 
3.5%
0.32 101
 
3.1%
0.3 94
 
2.9%
0.26 89
 
2.8%
0.4 87
 
2.7%
0.36 85
 
2.6%
0.33 80
 
2.5%
Other values (74) 2096
64.9%
ValueCountFrequency (%)
0 140
4.3%
0.01 36
 
1.1%
0.02 52
 
1.6%
0.03 31
 
1.0%
0.04 35
 
1.1%
0.05 23
 
0.7%
0.06 28
 
0.9%
0.07 32
 
1.0%
0.08 36
 
1.1%
0.09 32
 
1.0%
ValueCountFrequency (%)
1.66 1
 
< 0.1%
1 2
 
0.1%
0.99 1
 
< 0.1%
0.88 1
 
< 0.1%
0.81 1
 
< 0.1%
0.79 1
 
< 0.1%
0.78 1
 
< 0.1%
0.76 3
 
0.1%
0.75 1
 
< 0.1%
0.74 42
1.3%

residual sugar
Real number (ℝ)

Distinct230
Distinct (%)7.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.3036974
Minimum0.8
Maximum23.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size25.4 KiB
2024-09-06T15:29:58.594989image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.8
5-th percentile1.2
Q11.8
median2.3
Q35.4
95-th percentile13.9
Maximum23.5
Range22.7
Interquartile range (IQR)3.6

Descriptive statistics

Standard deviation4.074992
Coefficient of variation (CV)0.94685839
Kurtosis2.3524725
Mean4.3036974
Median Absolute Deviation (MAD)0.7
Skewness1.766483
Sum13909.55
Variance16.60556
MonotonicityNot monotonic
2024-09-06T15:29:58.921453image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 182
 
5.6%
1.8 171
 
5.3%
2.2 154
 
4.8%
2.1 151
 
4.7%
1.9 141
 
4.4%
1.6 130
 
4.0%
2.3 121
 
3.7%
1.7 119
 
3.7%
2.4 99
 
3.1%
1.4 98
 
3.0%
Other values (220) 1866
57.7%
ValueCountFrequency (%)
0.8 11
 
0.3%
0.9 16
 
0.5%
1 30
 
0.9%
1.1 56
1.7%
1.15 2
 
0.1%
1.2 80
2.5%
1.3 60
1.9%
1.35 1
 
< 0.1%
1.4 98
3.0%
1.45 3
 
0.1%
ValueCountFrequency (%)
23.5 1
 
< 0.1%
22 2
0.1%
20.8 1
 
< 0.1%
20.7 2
0.1%
19.8 2
0.1%
19.45 3
0.1%
19.4 1
 
< 0.1%
19.3 1
 
< 0.1%
19.25 1
 
< 0.1%
18.95 2
0.1%

chlorides
Real number (ℝ)

HIGH CORRELATION 

Distinct189
Distinct (%)5.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.066740099
Minimum0.012
Maximum0.611
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size25.4 KiB
2024-09-06T15:29:59.248919image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.012
5-th percentile0.031
Q10.043
median0.059
Q30.08
95-th percentile0.114
Maximum0.611
Range0.599
Interquartile range (IQR)0.037

Descriptive statistics

Standard deviation0.042284894
Coefficient of variation (CV)0.63357554
Kurtosis41.109553
Mean0.066740099
Median Absolute Deviation (MAD)0.018
Skewness5.0520105
Sum215.704
Variance0.0017880123
MonotonicityNot monotonic
2024-09-06T15:29:59.479969image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.044 91
 
2.8%
0.05 78
 
2.4%
0.04 76
 
2.4%
0.048 74
 
2.3%
0.047 71
 
2.2%
0.046 69
 
2.1%
0.045 68
 
2.1%
0.08 68
 
2.1%
0.038 66
 
2.0%
0.042 65
 
2.0%
Other values (179) 2506
77.5%
ValueCountFrequency (%)
0.012 2
 
0.1%
0.017 2
 
0.1%
0.018 1
 
< 0.1%
0.019 2
 
0.1%
0.02 4
 
0.1%
0.021 4
 
0.1%
0.022 8
0.2%
0.023 4
 
0.1%
0.024 8
0.2%
0.025 10
0.3%
ValueCountFrequency (%)
0.611 1
 
< 0.1%
0.61 1
 
< 0.1%
0.467 1
 
< 0.1%
0.464 1
 
< 0.1%
0.422 1
 
< 0.1%
0.415 3
0.1%
0.414 2
0.1%
0.413 1
 
< 0.1%
0.403 1
 
< 0.1%
0.401 1
 
< 0.1%

free sulfur dioxide
Real number (ℝ)

HIGH CORRELATION 

Distinct99
Distinct (%)3.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.460705
Minimum1
Maximum131
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size25.4 KiB
2024-09-06T15:29:59.725632image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5
Q112
median23
Q335
95-th percentile57
Maximum131
Range130
Interquartile range (IQR)23

Descriptive statistics

Standard deviation16.73179
Coefficient of variation (CV)0.6571613
Kurtosis0.81478768
Mean25.460705
Median Absolute Deviation (MAD)12
Skewness0.90396857
Sum82289
Variance279.9528
MonotonicityNot monotonic
2024-09-06T15:29:59.985960image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6 150
 
4.6%
5 114
 
3.5%
15 108
 
3.3%
10 102
 
3.2%
12 99
 
3.1%
17 93
 
2.9%
7 82
 
2.5%
16 80
 
2.5%
9 76
 
2.4%
11 75
 
2.3%
Other values (89) 2253
69.7%
ValueCountFrequency (%)
1 3
 
0.1%
2 1
 
< 0.1%
3 52
 
1.6%
4 43
 
1.3%
5 114
3.5%
5.5 1
 
< 0.1%
6 150
4.6%
7 82
2.5%
8 71
2.2%
9 76
2.4%
ValueCountFrequency (%)
131 1
< 0.1%
122.5 1
< 0.1%
88 1
< 0.1%
87 2
0.1%
83 2
0.1%
82.5 1
< 0.1%
82 1
< 0.1%
81 2
0.1%
80 1
< 0.1%
79 1
< 0.1%

total sulfur dioxide
Real number (ℝ)

HIGH CORRELATION 

Distinct259
Distinct (%)8.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean95.52367
Minimum6
Maximum366.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size25.4 KiB
2024-09-06T15:30:00.589299image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile14
Q138
median91
Q3144
95-th percentile201
Maximum366.5
Range360.5
Interquartile range (IQR)106

Descriptive statistics

Standard deviation62.29632
Coefficient of variation (CV)0.65215585
Kurtosis-0.77309119
Mean95.52367
Median Absolute Deviation (MAD)53
Skewness0.40364182
Sum308732.5
Variance3880.8315
MonotonicityNot monotonic
2024-09-06T15:30:00.821555image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
28 43
 
1.3%
24 38
 
1.2%
18 35
 
1.1%
15 35
 
1.1%
23 34
 
1.1%
20 33
 
1.0%
14 33
 
1.0%
31 32
 
1.0%
38 31
 
1.0%
27 30
 
0.9%
Other values (249) 2888
89.4%
ValueCountFrequency (%)
6 3
 
0.1%
7 4
 
0.1%
8 14
 
0.4%
9 14
 
0.4%
10 27
0.8%
11 26
0.8%
12 29
0.9%
13 28
0.9%
14 33
1.0%
15 35
1.1%
ValueCountFrequency (%)
366.5 1
 
< 0.1%
313 1
 
< 0.1%
289 1
 
< 0.1%
278 1
 
< 0.1%
272 1
 
< 0.1%
260 1
 
< 0.1%
255 1
 
< 0.1%
253 2
0.1%
252 1
 
< 0.1%
251 3
0.1%

density
Real number (ℝ)

HIGH CORRELATION 

Distinct503
Distinct (%)15.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.6759516
Minimum0.98815
Maximum100.369
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size25.4 KiB
2024-09-06T15:30:01.080503image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.98815
5-th percentile0.991
Q10.9936
median0.9958
Q30.9974
95-th percentile0.9996225
Maximum100.369
Range99.38085
Interquartile range (IQR)0.0038

Descriptive statistics

Standard deviation7.0672301
Coefficient of variation (CV)4.2168462
Kurtosis184.15766
Mean1.6759516
Median Absolute Deviation (MAD)0.0018
Skewness13.444222
Sum5416.6756
Variance49.945741
MonotonicityNot monotonic
2024-09-06T15:30:01.349716image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.9972 60
 
1.9%
0.9976 60
 
1.9%
0.998 53
 
1.6%
0.9968 50
 
1.5%
0.992 50
 
1.5%
0.9928 49
 
1.5%
0.9962 47
 
1.5%
0.9956 45
 
1.4%
0.9978 45
 
1.4%
0.9982 42
 
1.3%
Other values (493) 2731
84.5%
ValueCountFrequency (%)
0.98815 1
 
< 0.1%
0.9886 1
 
< 0.1%
0.989 1
 
< 0.1%
0.9892 3
 
0.1%
0.9893 6
0.2%
0.9894 6
0.2%
0.9896 6
0.2%
0.98965 1
 
< 0.1%
0.9898 11
0.3%
0.9899 3
 
0.1%
ValueCountFrequency (%)
100.369 2
0.1%
100.315 3
0.1%
100.289 1
 
< 0.1%
100.242 2
0.1%
100.055 1
 
< 0.1%
100.025 1
 
< 0.1%
100.024 1
 
< 0.1%
100.015 2
0.1%
100.012 1
 
< 0.1%
100.005 2
0.1%

pH
Real number (ℝ)

Distinct98
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.2580848
Minimum2.74
Maximum4.01
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size25.4 KiB
2024-09-06T15:30:01.586879image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum2.74
5-th percentile3
Q13.14
median3.26
Q33.37
95-th percentile3.53
Maximum4.01
Range1.27
Interquartile range (IQR)0.23

Descriptive statistics

Standard deviation0.16393324
Coefficient of variation (CV)0.050315831
Kurtosis0.22351151
Mean3.2580848
Median Absolute Deviation (MAD)0.11
Skewness0.2288162
Sum10530.13
Variance0.026874109
MonotonicityNot monotonic
2024-09-06T15:30:01.839191image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.3 98
 
3.0%
3.36 87
 
2.7%
3.32 83
 
2.6%
3.16 82
 
2.5%
3.28 81
 
2.5%
3.26 80
 
2.5%
3.15 80
 
2.5%
3.29 80
 
2.5%
3.22 79
 
2.4%
3.14 76
 
2.4%
Other values (88) 2406
74.4%
ValueCountFrequency (%)
2.74 2
 
0.1%
2.85 3
 
0.1%
2.86 3
 
0.1%
2.87 5
 
0.2%
2.88 4
 
0.1%
2.89 15
0.5%
2.9 3
 
0.1%
2.91 3
 
0.1%
2.92 8
0.2%
2.93 12
0.4%
ValueCountFrequency (%)
4.01 2
 
0.1%
3.9 2
 
0.1%
3.85 1
 
< 0.1%
3.82 1
 
< 0.1%
3.81 1
 
< 0.1%
3.78 2
 
0.1%
3.77 1
 
< 0.1%
3.75 1
 
< 0.1%
3.74 1
 
< 0.1%
3.72 6
0.2%

sulphates
Real number (ℝ)

HIGH CORRELATION 

Distinct108
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.57123453
Minimum0.25
Maximum2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size25.4 KiB
2024-09-06T15:30:02.069504image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.25
5-th percentile0.36
Q10.46
median0.55
Q30.65
95-th percentile0.8545
Maximum2
Range1.75
Interquartile range (IQR)0.19

Descriptive statistics

Standard deviation0.16840245
Coefficient of variation (CV)0.2948044
Kurtosis8.3821014
Mean0.57123453
Median Absolute Deviation (MAD)0.09
Skewness1.7887024
Sum1846.23
Variance0.028359385
MonotonicityNot monotonic
2024-09-06T15:30:02.345617image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.54 123
 
3.8%
0.5 102
 
3.2%
0.6 98
 
3.0%
0.58 95
 
2.9%
0.52 94
 
2.9%
0.44 91
 
2.8%
0.46 91
 
2.8%
0.62 90
 
2.8%
0.56 89
 
2.8%
0.53 88
 
2.7%
Other values (98) 2271
70.3%
ValueCountFrequency (%)
0.25 3
 
0.1%
0.27 5
 
0.2%
0.28 9
 
0.3%
0.29 9
 
0.3%
0.3 13
0.4%
0.31 4
 
0.1%
0.32 26
0.8%
0.33 28
0.9%
0.34 31
1.0%
0.35 32
1.0%
ValueCountFrequency (%)
2 1
 
< 0.1%
1.98 1
 
< 0.1%
1.95 2
0.1%
1.62 1
 
< 0.1%
1.61 1
 
< 0.1%
1.59 1
 
< 0.1%
1.56 1
 
< 0.1%
1.36 3
0.1%
1.34 1
 
< 0.1%
1.33 1
 
< 0.1%
Distinct69
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Memory size25.4 KiB
2024-09-06T15:30:02.758159image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length19
Median length5
Mean length3.3477723
Min length1

Characters and Unicode

Total characters10820
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique11 ?
Unique (%)0.3%

Sample

1st row8.8
2nd row9.5
3rd row10.1
4th row9.9
5th row9.9
ValueCountFrequency (%)
9.5 222
 
6.9%
9.4 181
 
5.6%
10 143
 
4.4%
9.2 141
 
4.4%
9.8 133
 
4.1%
10.5 132
 
4.1%
9.3 115
 
3.6%
9.6 114
 
3.5%
11 103
 
3.2%
9 99
 
3.1%
Other values (59) 1849
57.2%
2024-09-06T15:30:03.401286image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 2853
26.4%
1 2608
24.1%
9 1535
14.2%
0 922
 
8.5%
2 651
 
6.0%
5 479
 
4.4%
8 478
 
4.4%
4 393
 
3.6%
3 342
 
3.2%
6 291
 
2.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 10820
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 2853
26.4%
1 2608
24.1%
9 1535
14.2%
0 922
 
8.5%
2 651
 
6.0%
5 479
 
4.4%
8 478
 
4.4%
4 393
 
3.6%
3 342
 
3.2%
6 291
 
2.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 10820
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 2853
26.4%
1 2608
24.1%
9 1535
14.2%
0 922
 
8.5%
2 651
 
6.0%
5 479
 
4.4%
8 478
 
4.4%
4 393
 
3.6%
3 342
 
3.2%
6 291
 
2.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 10820
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 2853
26.4%
1 2608
24.1%
9 1535
14.2%
0 922
 
8.5%
2 651
 
6.0%
5 479
 
4.4%
8 478
 
4.4%
4 393
 
3.6%
3 342
 
3.2%
6 291
 
2.7%

quality
Real number (ℝ)

Distinct7
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.7602104
Minimum3
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size25.4 KiB
2024-09-06T15:30:03.564012image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile5
Q15
median6
Q36
95-th percentile7
Maximum9
Range6
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.88369863
Coefficient of variation (CV)0.1534143
Kurtosis0.34627055
Mean5.7602104
Median Absolute Deviation (MAD)1
Skewness0.23903334
Sum18617
Variance0.78092326
MonotonicityNot monotonic
2024-09-06T15:30:03.738577image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
6 1335
41.3%
5 1167
36.1%
7 501
 
15.5%
4 118
 
3.7%
8 86
 
2.7%
3 20
 
0.6%
9 5
 
0.2%
ValueCountFrequency (%)
3 20
 
0.6%
4 118
 
3.7%
5 1167
36.1%
6 1335
41.3%
7 501
 
15.5%
8 86
 
2.7%
9 5
 
0.2%
ValueCountFrequency (%)
9 5
 
0.2%
8 86
 
2.7%
7 501
 
15.5%
6 1335
41.3%
5 1167
36.1%
4 118
 
3.7%
3 20
 
0.6%

Interactions

2024-09-06T15:29:53.897751image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-06T15:29:31.720022image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-06T15:29:34.278455image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-06T15:29:37.269515image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-06T15:29:39.345871image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-06T15:29:41.301368image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-06T15:29:43.390516image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-06T15:29:45.415939image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-06T15:29:47.500798image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-06T15:29:49.904072image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-06T15:29:51.851695image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-06T15:29:54.064603image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-06T15:29:31.896915image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-06T15:29:34.461337image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-06T15:29:37.457114image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-06T15:29:39.509588image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-06T15:29:41.467292image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-06T15:29:43.565013image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-06T15:29:45.602582image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-06T15:29:47.675433image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-06T15:29:50.079155image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-06T15:29:52.028308image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-06T15:29:54.248375image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-06T15:29:32.113925image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-06T15:29:35.195395image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-06T15:29:37.644629image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-06T15:29:39.698795image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-06T15:29:41.665697image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-06T15:29:43.775235image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-06T15:29:45.795182image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-06T15:29:47.862858image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-06T15:29:50.262846image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-06T15:29:52.214581image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-06T15:29:54.422586image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-06T15:29:32.303541image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-06T15:29:35.399154image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-06T15:29:37.825402image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-06T15:29:39.875350image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-06T15:29:41.850872image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-06T15:29:43.984862image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-06T15:29:45.983710image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-06T15:29:48.476687image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-06T15:29:50.440970image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-06T15:29:52.391539image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-06T15:29:54.582072image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-06T15:29:32.465131image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-06T15:29:35.642796image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-06T15:29:37.993425image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-06T15:29:40.027369image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-06T15:29:42.094634image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-06T15:29:44.145833image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-06T15:29:46.163299image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-06T15:29:48.627805image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-06T15:29:50.599715image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-06T15:29:52.558873image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-06T15:29:54.747931image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-06T15:29:32.871636image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-06T15:29:35.865356image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-06T15:29:38.173481image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-06T15:29:40.264146image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-06T15:29:42.262900image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-06T15:29:44.325734image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-06T15:29:46.364947image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-06T15:29:48.819011image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-06T15:29:50.783072image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-06T15:29:52.745789image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-06T15:29:54.915040image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-06T15:29:33.051779image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-06T15:29:36.092202image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-06T15:29:38.379616image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-06T15:29:40.435870image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-06T15:29:42.463961image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-06T15:29:44.506150image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-06T15:29:46.560853image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-06T15:29:49.013750image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-06T15:29:50.964909image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-06T15:29:52.937907image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-06T15:29:55.093276image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-06T15:29:33.298793image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-06T15:29:36.310733image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-06T15:29:38.571941image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-06T15:29:40.618180image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-06T15:29:42.665058image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-06T15:29:44.699711image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-06T15:29:46.759825image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-06T15:29:49.192382image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-06T15:29:51.148486image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-06T15:29:53.125132image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-06T15:29:55.251559image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-06T15:29:33.523967image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-06T15:29:36.637847image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-06T15:29:38.742983image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-06T15:29:40.775794image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-06T15:29:42.839639image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-06T15:29:44.864730image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-06T15:29:46.932269image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-06T15:29:49.361770image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-06T15:29:51.310134image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-06T15:29:53.304764image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-06T15:29:55.421781image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-06T15:29:33.912017image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-06T15:29:36.880605image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-06T15:29:38.975528image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-06T15:29:40.959685image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-06T15:29:43.014181image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-06T15:29:45.043710image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-06T15:29:47.132215image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-06T15:29:49.544760image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-06T15:29:51.472635image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-06T15:29:53.499799image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-06T15:29:55.608459image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-06T15:29:34.112650image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-06T15:29:37.080499image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-06T15:29:39.164358image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-06T15:29:41.135994image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-06T15:29:43.210789image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-06T15:29:45.243598image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-06T15:29:47.326555image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-06T15:29:49.729604image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-06T15:29:51.660046image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-06T15:29:53.730124image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Correlations

2024-09-06T15:30:03.916527image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
chloridescitric aciddensityfixed acidityfree sulfur dioxidepHqualityresidual sugarsulphatestotal sulfur dioxidetypevolatile acidity
chlorides1.000-0.1580.5600.408-0.4200.166-0.255-0.0890.468-0.5380.7020.575
citric acid-0.1581.0000.0910.3550.157-0.4650.1730.156-0.0080.2190.396-0.478
density0.5600.0911.0000.488-0.102-0.044-0.2830.4540.332-0.1620.0550.346
fixed acidity0.4080.3550.4881.000-0.334-0.363-0.038-0.0250.286-0.3570.4290.178
free sulfur dioxide-0.4200.157-0.102-0.3341.000-0.1640.1010.365-0.3170.8120.571-0.438
pH0.166-0.465-0.044-0.363-0.1641.0000.050-0.1710.238-0.3040.3220.258
quality-0.2550.173-0.283-0.0380.1010.0501.000-0.0250.0880.0160.162-0.320
residual sugar-0.0890.1560.454-0.0250.365-0.171-0.0251.000-0.1290.3800.491-0.079
sulphates0.468-0.0080.3320.286-0.3170.2380.088-0.1291.000-0.4180.5220.308
total sulfur dioxide-0.5380.219-0.162-0.3570.812-0.3040.0160.380-0.4181.0000.813-0.508
type0.7020.3960.0550.4290.5710.3220.1620.4910.5220.8131.0000.664
volatile acidity0.575-0.4780.3460.178-0.4380.258-0.320-0.0790.308-0.5080.6641.000

Missing values

2024-09-06T15:29:55.919492image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-09-06T15:29:56.293452image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

typefixed acidityvolatile aciditycitric acidresidual sugarchloridesfree sulfur dioxidetotal sulfur dioxidedensitypHsulphatesalcoholquality
0Riesling7.00.270.3620.70.04545.0170.01.00103.000.458.86
1Riesling6.30.300.341.60.04914.0132.00.99403.300.499.56
2Riesling8.10.280.406.90.05030.097.00.99513.260.4410.16
3Riesling7.20.230.328.50.05847.0186.00.99563.190.409.96
4Riesling7.20.230.328.50.05847.0186.00.99563.190.409.96
5Riesling8.10.280.406.90.05030.097.00.99513.260.4410.16
6Riesling6.20.320.167.00.04530.0136.00.99493.180.479.66
7Riesling7.00.270.3620.70.04545.0170.01.00103.000.458.86
8Riesling6.30.300.341.60.04914.0132.00.99403.300.499.56
9Riesling8.10.220.431.50.04428.0129.00.99383.220.45116
typefixed acidityvolatile aciditycitric acidresidual sugarchloridesfree sulfur dioxidetotal sulfur dioxidedensitypHsulphatesalcoholquality
3222Garnacha6.60.7250.207.80.07329.079.00.997703.290.549.25
3223Garnacha6.30.5500.151.80.07726.035.00.993143.320.8211.66
3224Garnacha5.40.7400.091.70.08916.026.00.994023.670.5611.66
3225Garnacha6.30.5100.132.30.07629.040.00.995743.420.75116
3226Garnacha6.80.6200.081.90.06828.038.00.996513.420.829.56
3227Garnacha6.20.6000.082.00.09032.044.00.994903.450.5810.55
3228Garnacha5.90.5500.102.20.06239.051.00.995123.520.7611.26
3229Garnacha6.30.5100.132.30.07629.040.00.995743.420.75116
3230Garnacha5.90.6450.122.00.07532.044.00.995473.570.7110.25
3231Garnacha6.00.3100.473.60.06718.042.00.995493.390.66116

Duplicate rows

Most frequently occurring

typefixed acidityvolatile aciditycitric acidresidual sugarchloridesfree sulfur dioxidetotal sulfur dioxidedensitypHsulphatesalcoholquality# duplicates
22Garnacha6.70.4600.241.700.07718.034.00.994803.390.6010.664
52Garnacha7.20.3600.462.100.07424.044.00.995343.400.851174
63Garnacha7.20.6950.132.000.07612.020.00.995463.290.5410.154
81Garnacha7.50.5100.021.700.08413.031.00.995383.360.5410.564
325Riesling7.10.2600.3414.400.06735.0189.00.998603.070.539.174
337Riesling7.20.2300.3814.300.05855.0194.00.997903.090.44964
339Riesling7.20.2300.3914.200.05849.0192.00.997902.980.48974
363Riesling7.30.2500.3613.100.05035.0200.00.998603.040.468.974
380Riesling7.40.2500.3613.200.06753.0178.00.997603.010.48964
414Riesling7.80.3000.2916.850.05423.0135.00.999803.160.38964